Evaluating Model Fit in Bayesian Confirmatory Factor Analysis With Large Samples: Simulation Study Introducing the BRMSEA
| Autor: | Hoofs, Huub, van de Schoot, Rens, Jansen, Nicole W.H., Kant, IJmert, Leerstoel Hoijtink, Methodology and statistics for the behavioural and social sciences |
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| Přispěvatelé: | Epidemiologie, RS: CAPHRI - R3 - Functioning, Participating and Rehabilitation, Promovendi PHPC, Leerstoel Hoijtink, Methodology and statistics for the behavioural and social sciences |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
validity
STRUCTURAL EQUATION MODELS MISSPECIFICATION Bayesian probability Posterior probability INDEXES factor analysis 050109 social psychology Education Bayesian procedures 0504 sociology Goodness of fit Frequentist inference Statistics GOODNESS-OF-FIT Developmental and Educational Psychology Econometrics JOB CONTENT QUESTIONNAIRE 0501 psychology and cognitive sciences Bayesian average Applied Psychology Mathematics Applied Mathematics 05 social sciences 050401 social sciences methods Articles simulation Confirmatory factor analysis Bayesian statistics model fit SIZE Sample size determination RELIABILITY SENSITIVITY RMSEA |
| Zdroj: | Educational and Psychological Measurement, 78(4), 537-568. SAGE Publications Inc. Educational and Psychological Measurement, 78(4), 537. SAGE Publications Inc. Educational and Psychological Measurement |
| ISSN: | 0013-1644 |
| Popis: | Bayesian confirmatory factor analysis (CFA) offers an alternative to frequentist CFA based on, for example, maximum likelihood estimation for the assessment of reliability and validity of educational and psychological measures. For increasing sample sizes, however, the applicability of current fit statistics evaluating model fit within Bayesian CFA is limited. We propose, therefore, a Bayesian variant of the root mean square error of approximation (RMSEA), the BRMSEA. A simulation study was performed with variations in model misspecification, factor loading magnitude, number of indicators, number of factors, and sample size. This showed that the 90% posterior probability interval of the BRMSEA is valid for evaluating model fit in large samples ( N≥ 1,000), using cutoff values for the lower ( |
| Databáze: | OpenAIRE |
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